Job Description
The Autonomy Behaviors team is responsible for producing our vehicle’s driving trajectory. As a Principal ML Engineer, you will lead the technical development of machine learning algorithms for our next-generation ML Planner. Your impact will range from delivering innovations for immediate milestones to pioneering exploratory projects. You will collaborate closely with teams specializing in Foundation Models, Perception, Simulation, and Safety Validation, in influencing our autonomy stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company.
Qualifications
- BS, MS, or PhD degree in computer science or related field
- Proven track record of technical leadership in large-scale AI organizations
- Significant experience with training and deploying Deep Learning models to production
- Fluency in C++ or Fluency in Python with a basic understanding of C++
- Extensive experience with programming and algorithm design-Strong mathematics skills-10+ years of experience
Bonus Qualifications
- Conference or Journal publications in Machine Learning or Robotics related venues
- Prior experience with Prediction and Planning for autonomous vehicles
Responsibilities
- Set the technical direction of ML development for the future of our Planner
- Push the frontiers of imitation learning, reinforcement learning, and model scaling to develop new ML models for our onboard Planner
- Identify and drive force-multipliers for ML development, including metrics, data, and training frameworks
- Develop new methods to leverage Zoox’s Foundation Models into the ML Planner
- Provide technical mentorship to the broader group of ML developers at Zoox
- Collaborate with engineers across Autonomy Software, Simulation, System Design and Mission Assurance, and other departments to solve the overall Autonomous Driving problem in complex urban environments
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
Responsibilities
Responsibilities
- Set the technical direction of ML development for the future of our Planner
- Push the frontiers of imitation learning, reinforcement learning, and model scaling to develop new ML models for our onboard Planner
- Identify and drive force-multipliers for ML development, including metrics, data, and training frameworks
- Develop new methods to leverage Zoox’s Foundation Models into the ML Planner
- Provide technical mentorship to the broader group of ML developers at Zoox
- Collaborate with engineers across Autonomy Software, Simulation, System Design and Mission Assurance, and other departments to solve the overall Autonomous Driving problem in complex urban environments
Requirements
Qualifications
- BS, MS, or PhD degree in computer science or related field
- Proven track record of technical leadership in large-scale AI organizations
- Significant experience with training and deploying Deep Learning models to production
- Fluency in C++ or Fluency in Python with a basic understanding of C++
- Extensive experience with programming and algorithm design-Strong mathematics skills-10+ years of experience
Bonus Qualifications
- Conference or Journal publications in Machine Learning or Robotics related venues
- Prior experience with Prediction and Planning for autonomous vehicles
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